Modulation of innate and adaptive cellular immunity relevant to HIV-1 vaccine design by seminal plasma
Bibliographic record
Abstract
OBJECTIVES: Mucosal exposure to HIV-1 infection generally occurs in the presence of semen. Immunomodulation by seminal plasma is well described in the reproductive biology literature. Little is known, however, about the impact of seminal plasma on innate and adaptive anti-HIV-1 cellular immunity. DESIGN: The study investigated the effects of seminal plasma on immune responses considered important for prophylactic HIV-1 vaccine development, namely innate and adaptive cellular immunity mediated by natural killer (NK) cells and T cells, respectively. METHODS: The ability of seminal plasma to modulate direct, antibody-dependent and cytokine-stimulated NK cell activation was assessed utilizing intracellular cytokine staining. Direct and antibody-dependent cellular cytotoxicity was assessed using lactate dehydrogenase release assays. The effects of seminal plasma on T-cell activation upon stimulation with staphylococcus enterotoxin B or HIV-1 Gag peptides were assessed by intracellular cytokine staining. The impact of seminal plasma on redirected cytolysis mediated by T cells was measured using lactate dehydrogenase release assays. RESULTS: Both direct and antibody-dependent NK cell activation were dramatically impaired by the presence of either HIV-1-uninfected or HIV-1-infected seminal plasma in a dose-dependent manner. Additionally, seminal plasma suppressed both direct and antibody-dependent NK cell-mediated cytolysis, including anti-HIV-1 antibody-dependent cytolysis of gp120-pulsed CEM.NKr-CCR5 cells. Finally, seminal plasma attenuated both HIV-1 Gag-specific and staphylococcus enterotoxin B-induced CTL activation. CONCLUSIONS: Semen contains potent immunosuppressors of both NK cell and CD8 T-cell-mediated anti-HIV-1 immune responses. This could impede attempts to provide vaccine-induced immunity to HIV-1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".